Velocity spectrum imaging using radial k-t SPIRiT
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WORKSHOP PRESENTATION
Open Access
Velocity spectrum imaging using radial k-t SPIRiT Claudio Santelli1,2*, Sebastian Kozerke2,1, Tobias Schaeffter1 From 15th Annual SCMR Scientific Sessions Orlando, FL, USA. 2-5 February 2012 Background Fourier velocity encoding (FVE) [P.R.Moran,MRI (1),1982] assesses the distribution of velocities within a voxel by acquiring a range of velocity encodes (k v ) points. The ability to measure intra-voxel phase dispersion, however, comes at the expense of clinically infeasible scan times. We have recently extended [C.Santelli, ESMRMB(345),2011] the auto-calibrating parallel imaging technique SPIRiT [M.Lustig,MRM(64),2010] to exploit temporal correlations in dynamic k-t signal space and successfully applied it to radially undersampled FVE data. Prior assumption of Gaussian velocity spectra additionally allows undersampling along the velocity encoding dimensions [P.Dyverfeldt,MRM (56),2006]. In this work, a scheme is proposed to nonuniformly undersample the kv-axes in addition to undersampling k-t space for reconstructing mean and standard deviation (SD) of the velocity spectra for each voxel in aortic flow measurements. Methods Acquisition
2D radial (FOV=250mmx250mm) fully sampled cine FVE data of the aortic arch for 3 orthogonal velocity components was obtained from 5 healthy volunteers on a 3T Philips Achieva scanner (Philips Healthcare, Best, The Netherlands) using a 6 element receive array. Three different first gradient moments corresponding to encoding velocities of 25cm/s, 50cm/s and 200cm/s were applied along with a reference point (kv=0). Undersampled radial data sets were obtained by separately regridding these 4-point measurements onto Golden-angle profiles (Fig.1a).
1 Division of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK Full list of author information is available at the end of the article
Reconstruction
The interpolation operator G, enforcing consistency between calibration data from a fully sampled centre of k-space and reconstructed Cartesian k-space points, x, is extended for dynamic MRI by including temporal correlations between adjacent data frames (Fig.1b). Data consistency is imposed using gridding-operator D (Fig.1a). Then, x is recovered by solving the minimization problem in Fig.1d). Reconstruction was performed for every kv-point separately using dedicated software implemented in Matlab (Natick,MA,USA). A 7x7x3 neighborhood in kx-ky-t space was chosen for the k-t space interpolation kernel. The weights were calculated from a 30x30x (nr cardiac phases) calibration area (Fig.1c). Mean and SD of velocity distributions were calculated for the resulting coil-combined images.
Results Fig.2a) compares the mean root-mean-square error (RMSE) of the reconstructed mean velocities and SDs in the aortic arch for different undersampling factors and for each flow direction (M-P-S). Fig.2b) shows in-plane streamlines reconstructed from the acquired mean velocities and turbulence intensity maps calculated from SD values. Conclusions A
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